Podcasts about usf's applied economics master

Fear pervades dictatorial regimes. Citizens fear leaders, the regime's agents fear superiors, and leaders fear the masses. The ubiquity of fear in such regimes gives rise to the "dictator's dilemma," where autocrats do not know the level of opposition they face and cannot effectively neutralize domestic threats to their rule. The dilemma has led scholars to believe that autocracies are likely to be short-lived. Yet, some autocracies have found ways to mitigate the dictator's dilemma. As Martin K. Dimitrov shows in Dictatorship and Information: Authoritarian Regime Resilience in Communist Europe and China (Oxford UP, 2023), substantial variability exists in the survival of nondemocratic regimes, with single-party polities having the longest average duration. Offering a systematic theory of the institutional solutions to the dictator's dilemma, Dimitrov argues that single-party autocracies have fostered channels that allow for the confidential vertical transmission of information, while also solving the problems associated with distorted information. To explain how this all works, Dimitrov focuses on communist regimes, which have the longest average lifespan among single-party autocracies and have developed the most sophisticated information-gathering institutions. Communist regimes face a variety of threats, but the main one is the masses. Dimitrov therefore examines the origins, evolution, and internal logic of the information-collection ecosystem established by communist states to monitor popular dissent. Drawing from a rich base of evidence across multiple communist regimes and nearly 100 interviews, Dimitrov reshapes our understanding of how autocrats learn--or fail to learn--about the societies they rule, and how they maintain--or lose--power. Listeners interested in how authoritarian regimes gather information and use it to maintain political control should also check out the NBN interviews with Iza Ding, on how China's bureaucrats make a show of responsiveness even when they can't deliver, Jeremy Wallace, on the role of quantification in China's authoritarianism, Daniel Treisman, on how dictators around the world try to control their public image, Jennifer Pan, on how China uses its limited welfare state to hold power, journalists Josh Chin and Liza Lin on China's surveillance state, and Yao Li, Manfred Elfstrom, and Lynette Ong on China's protests. Martin K. Dimitrov is Professor of Political Science at Tulane University.  Peter Lorentzen is economics professor at the University of San Francisco. He heads USF's Applied Economics Master's program, which focuses on the digital economy. His research is mainly on China's political economy. Learn more about your ad choices. Visit megaphone.fm/adchoices

Fear pervades dictatorial regimes. Citizens fear leaders, the regime's agents fear superiors, and leaders fear the masses. The ubiquity of fear in such regimes gives rise to the "dictator's dilemma," where autocrats do not know the level of opposition they face and cannot effectively neutralize domestic threats to their rule. The dilemma has led scholars to believe that autocracies are likely to be short-lived. Yet, some autocracies have found ways to mitigate the dictator's dilemma. As Martin K. Dimitrov shows in Dictatorship and Information: Authoritarian Regime Resilience in Communist Europe and China (Oxford UP, 2023), substantial variability exists in the survival of nondemocratic regimes, with single-party polities having the longest average duration. Offering a systematic theory of the institutional solutions to the dictator's dilemma, Dimitrov argues that single-party autocracies have fostered channels that allow for the confidential vertical transmission of information, while also solving the problems associated with distorted information. To explain how this all works, Dimitrov focuses on communist regimes, which have the longest average lifespan among single-party autocracies and have developed the most sophisticated information-gathering institutions. Communist regimes face a variety of threats, but the main one is the masses. Dimitrov therefore examines the origins, evolution, and internal logic of the information-collection ecosystem established by communist states to monitor popular dissent. Drawing from a rich base of evidence across multiple communist regimes and nearly 100 interviews, Dimitrov reshapes our understanding of how autocrats learn--or fail to learn--about the societies they rule, and how they maintain--or lose--power. Listeners interested in how authoritarian regimes gather information and use it to maintain political control should also check out the NBN interviews with Iza Ding, on how China's bureaucrats make a show of responsiveness even when they can't deliver, Jeremy Wallace, on the role of quantification in China's authoritarianism, Daniel Treisman, on how dictators around the world try to control their public image, Jennifer Pan, on how China uses its limited welfare state to hold power, journalists Josh Chin and Liza Lin on China's surveillance state, and Yao Li, Manfred Elfstrom, and Lynette Ong on China's protests. Martin K. Dimitrov is Professor of Political Science at Tulane University.  Peter Lorentzen is economics professor at the University of San Francisco. He heads USF's Applied Economics Master's program, which focuses on the digital economy. His research is mainly on China's political economy.

Fear pervades dictatorial regimes. Citizens fear leaders, the regime's agents fear superiors, and leaders fear the masses. The ubiquity of fear in such regimes gives rise to the "dictator's dilemma," where autocrats do not know the level of opposition they face and cannot effectively neutralize domestic threats to their rule. The dilemma has led scholars to believe that autocracies are likely to be short-lived. Yet, some autocracies have found ways to mitigate the dictator's dilemma. As Martin K. Dimitrov shows in Dictatorship and Information: Authoritarian Regime Resilience in Communist Europe and China (Oxford UP, 2023), substantial variability exists in the survival of nondemocratic regimes, with single-party polities having the longest average duration. Offering a systematic theory of the institutional solutions to the dictator's dilemma, Dimitrov argues that single-party autocracies have fostered channels that allow for the confidential vertical transmission of information, while also solving the problems associated with distorted information. To explain how this all works, Dimitrov focuses on communist regimes, which have the longest average lifespan among single-party autocracies and have developed the most sophisticated information-gathering institutions. Communist regimes face a variety of threats, but the main one is the masses. Dimitrov therefore examines the origins, evolution, and internal logic of the information-collection ecosystem established by communist states to monitor popular dissent. Drawing from a rich base of evidence across multiple communist regimes and nearly 100 interviews, Dimitrov reshapes our understanding of how autocrats learn--or fail to learn--about the societies they rule, and how they maintain--or lose--power. Listeners interested in how authoritarian regimes gather information and use it to maintain political control should also check out the NBN interviews with Iza Ding, on how China's bureaucrats make a show of responsiveness even when they can't deliver, Jeremy Wallace, on the role of quantification in China's authoritarianism, Daniel Treisman, on how dictators around the world try to control their public image, Jennifer Pan, on how China uses its limited welfare state to hold power, journalists Josh Chin and Liza Lin on China's surveillance state, and Yao Li, Manfred Elfstrom, and Lynette Ong on China's protests. Martin K. Dimitrov is Professor of Political Science at Tulane University.  Peter Lorentzen is economics professor at the University of San Francisco. He heads USF's Applied Economics Master's program, which focuses on the digital economy. His research is mainly on China's political economy. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/communications

Fear pervades dictatorial regimes. Citizens fear leaders, the regime's agents fear superiors, and leaders fear the masses. The ubiquity of fear in such regimes gives rise to the "dictator's dilemma," where autocrats do not know the level of opposition they face and cannot effectively neutralize domestic threats to their rule. The dilemma has led scholars to believe that autocracies are likely to be short-lived. Yet, some autocracies have found ways to mitigate the dictator's dilemma. As Martin K. Dimitrov shows in Dictatorship and Information: Authoritarian Regime Resilience in Communist Europe and China (Oxford UP, 2023), substantial variability exists in the survival of nondemocratic regimes, with single-party polities having the longest average duration. Offering a systematic theory of the institutional solutions to the dictator's dilemma, Dimitrov argues that single-party autocracies have fostered channels that allow for the confidential vertical transmission of information, while also solving the problems associated with distorted information. To explain how this all works, Dimitrov focuses on communist regimes, which have the longest average lifespan among single-party autocracies and have developed the most sophisticated information-gathering institutions. Communist regimes face a variety of threats, but the main one is the masses. Dimitrov therefore examines the origins, evolution, and internal logic of the information-collection ecosystem established by communist states to monitor popular dissent. Drawing from a rich base of evidence across multiple communist regimes and nearly 100 interviews, Dimitrov reshapes our understanding of how autocrats learn--or fail to learn--about the societies they rule, and how they maintain--or lose--power. Listeners interested in how authoritarian regimes gather information and use it to maintain political control should also check out the NBN interviews with Iza Ding, on how China's bureaucrats make a show of responsiveness even when they can't deliver, Jeremy Wallace, on the role of quantification in China's authoritarianism, Daniel Treisman, on how dictators around the world try to control their public image, Jennifer Pan, on how China uses its limited welfare state to hold power, journalists Josh Chin and Liza Lin on China's surveillance state, and Yao Li, Manfred Elfstrom, and Lynette Ong on China's protests. Martin K. Dimitrov is Professor of Political Science at Tulane University.  Peter Lorentzen is economics professor at the University of San Francisco. He heads USF's Applied Economics Master's program, which focuses on the digital economy. His research is mainly on China's political economy. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/economics

Fear pervades dictatorial regimes. Citizens fear leaders, the regime's agents fear superiors, and leaders fear the masses. The ubiquity of fear in such regimes gives rise to the "dictator's dilemma," where autocrats do not know the level of opposition they face and cannot effectively neutralize domestic threats to their rule. The dilemma has led scholars to believe that autocracies are likely to be short-lived. Yet, some autocracies have found ways to mitigate the dictator's dilemma. As Martin K. Dimitrov shows in Dictatorship and Information: Authoritarian Regime Resilience in Communist Europe and China (Oxford UP, 2023), substantial variability exists in the survival of nondemocratic regimes, with single-party polities having the longest average duration. Offering a systematic theory of the institutional solutions to the dictator's dilemma, Dimitrov argues that single-party autocracies have fostered channels that allow for the confidential vertical transmission of information, while also solving the problems associated with distorted information. To explain how this all works, Dimitrov focuses on communist regimes, which have the longest average lifespan among single-party autocracies and have developed the most sophisticated information-gathering institutions. Communist regimes face a variety of threats, but the main one is the masses. Dimitrov therefore examines the origins, evolution, and internal logic of the information-collection ecosystem established by communist states to monitor popular dissent. Drawing from a rich base of evidence across multiple communist regimes and nearly 100 interviews, Dimitrov reshapes our understanding of how autocrats learn--or fail to learn--about the societies they rule, and how they maintain--or lose--power. Listeners interested in how authoritarian regimes gather information and use it to maintain political control should also check out the NBN interviews with Iza Ding, on how China's bureaucrats make a show of responsiveness even when they can't deliver, Jeremy Wallace, on the role of quantification in China's authoritarianism, Daniel Treisman, on how dictators around the world try to control their public image, Jennifer Pan, on how China uses its limited welfare state to hold power, journalists Josh Chin and Liza Lin on China's surveillance state, and Yao Li, Manfred Elfstrom, and Lynette Ong on China's protests. Martin K. Dimitrov is Professor of Political Science at Tulane University.  Peter Lorentzen is economics professor at the University of San Francisco. He heads USF's Applied Economics Master's program, which focuses on the digital economy. His research is mainly on China's political economy. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/eastern-european-studies

What does the state do when public expectations exceed its governing capacity? The Performative State: Public Scrutiny and Environmental Governance in China (Cornell, 2022) shows how the state can shape public perceptions and defuse crises through the theatrical deployment of language, symbols, and gestures of good governance—performative governance. Iza Ding unpacks the black box of street-level bureaucracy in China through ethnographic participation, in-depth interviews, and public opinion surveys. She demonstrates in vivid detail how China's environmental bureaucrats deal with intense public scrutiny over pollution when they lack the authority to actually improve the physical environment. They assuage public outrage by appearing responsive, benevolent, and humble. But performative governance is hard work. Environmental bureaucrats paradoxically work themselves to exhaustion even when they cannot effectively implement environmental policies. Instead of achieving "performance legitimacy" by delivering material improvements, the state can shape public opinion through the theatrical performance of goodwill and sincere effort. The Performative State also explains when performative governance fails at impressing its audience and when governance becomes less performative and more substantive. Ding focuses on Chinese evidence but her theory travels: comparisons with Vietnam and the United States show that all states, democratic and authoritarian alike, engage in performative governance. Iza Ding is an Assistant Professor in the Political Science Department at the University of Pittsburgh. She received her PhD from Harvard University.  Her work has appeared in World Politics, the China Quarterly, Comparative Political Studies, and other academic journals. Peter Lorentzen is economics professor at the University of San Francisco. He heads USF's Applied Economics Master's program, which focuses on the digital economy. His research is mainly on China's political economy. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/economics

What does the state do when public expectations exceed its governing capacity? The Performative State: Public Scrutiny and Environmental Governance in China (Cornell, 2022) shows how the state can shape public perceptions and defuse crises through the theatrical deployment of language, symbols, and gestures of good governance—performative governance. Iza Ding unpacks the black box of street-level bureaucracy in China through ethnographic participation, in-depth interviews, and public opinion surveys. She demonstrates in vivid detail how China's environmental bureaucrats deal with intense public scrutiny over pollution when they lack the authority to actually improve the physical environment. They assuage public outrage by appearing responsive, benevolent, and humble. But performative governance is hard work. Environmental bureaucrats paradoxically work themselves to exhaustion even when they cannot effectively implement environmental policies. Instead of achieving "performance legitimacy" by delivering material improvements, the state can shape public opinion through the theatrical performance of goodwill and sincere effort. The Performative State also explains when performative governance fails at impressing its audience and when governance becomes less performative and more substantive. Ding focuses on Chinese evidence but her theory travels: comparisons with Vietnam and the United States show that all states, democratic and authoritarian alike, engage in performative governance. Iza Ding is an Assistant Professor in the Political Science Department at the University of Pittsburgh. She received her PhD from Harvard University.  Her work has appeared in World Politics, the China Quarterly, Comparative Political Studies, and other academic journals. Peter Lorentzen is economics professor at the University of San Francisco. He heads USF's Applied Economics Master's program, which focuses on the digital economy. His research is mainly on China's political economy. Learn more about your ad choices. Visit megaphone.fm/adchoices

What does the state do when public expectations exceed its governing capacity? The Performative State: Public Scrutiny and Environmental Governance in China (Cornell, 2022) shows how the state can shape public perceptions and defuse crises through the theatrical deployment of language, symbols, and gestures of good governance—performative governance. Iza Ding unpacks the black box of street-level bureaucracy in China through ethnographic participation, in-depth interviews, and public opinion surveys. She demonstrates in vivid detail how China's environmental bureaucrats deal with intense public scrutiny over pollution when they lack the authority to actually improve the physical environment. They assuage public outrage by appearing responsive, benevolent, and humble. But performative governance is hard work. Environmental bureaucrats paradoxically work themselves to exhaustion even when they cannot effectively implement environmental policies. Instead of achieving "performance legitimacy" by delivering material improvements, the state can shape public opinion through the theatrical performance of goodwill and sincere effort. The Performative State also explains when performative governance fails at impressing its audience and when governance becomes less performative and more substantive. Ding focuses on Chinese evidence but her theory travels: comparisons with Vietnam and the United States show that all states, democratic and authoritarian alike, engage in performative governance. Iza Ding is an Assistant Professor in the Political Science Department at the University of Pittsburgh. She received her PhD from Harvard University.  Her work has appeared in World Politics, the China Quarterly, Comparative Political Studies, and other academic journals. Peter Lorentzen is economics professor at the University of San Francisco. He heads USF's Applied Economics Master's program, which focuses on the digital economy. His research is mainly on China's political economy. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/public-policy

Disruption resulting from the proliferation of AI is coming. The authors of the bestselling Prediction Machines describe what you can do to prepare. Banking and finance, pharmaceuticals, automotive, medical technology, retail. Artificial intelligence (AI) has made its way into many industries around the world. But the truth is, it has just begun its odyssey toward cheaper, better, and faster predictions to drive strategic business decisions--powering and accelerating business. When prediction is taken to the max, industries transform. The disruption that comes with such transformation is yet to be felt--but it is coming. How do businesses prepare?  In Prediction Machines, eminent economists Ajay Agrawal, Joshua Gans, and Avi Goldfarb explained the simple yet game-changing economics of AI. Now, in Power and Prediction: The Disruptive Economics of Artificial Intelligence (HBR Press, 2022), they go further to reveal AI as a prediction technology directly impacting decision-making and to teach businesses how to identify disruptive opportunities and threats resulting from AI. Their exhaustive study of new developments in artificial intelligence and the past history of how technologies have disrupted industries highlights the striking phase we are now in: after witnessing the power of this new technology and before its widespread adoption--what they call "the Between Times." While there continue to be important opportunities for businesses, there are also threats of disruption. As prediction machines improve, old ways of doing things will be upended. Also, the process by which AI filters into the many systems involved in application is very uneven. That process will have winners and losers. How can businesses leverage, or protect, their positions? Filled with illuminating insights, rich examples, and practical advice, Power and Prediction is the must-read guide for any business leader or policy maker on how to make the coming AI disruptions work for you rather than against you. Interviewee Avi Goldfarb is the Rotman Chair in Artificial Intelligence and Healthcare and a professor of marketing at the Rotman School of Management, University of Toronto. Avi is also Chief Data Scientist at the Creative Destruction Lab and the CDL Rapid Screening Consortium, a faculty affiliate at the Vector Institute and the Schwartz-Reisman Institute for Technology and Society, and a Research Associate at the National Bureau of Economic Research. Avi's research focuses on the opportunities and challenges of the digital economy. He has published academic articles in marketing, statistics, law, management, medicine, political science, refugee studies, physics, computing, and economics. Avi is a former Senior Editor at Marketing Science. His work on online advertising won the INFORMS Society of Marketing Science Long Term Impact Award. He testified before the U.S. Senate Judiciary Committee on competition and privacy in digital advertising. His work has been referenced in White House reports, European Commission documents, the New York Times, the Economist, and elsewhere. Peter Lorentzen is economics professor at the University of San Francisco. He heads USF's Applied Economics Master's program, which focuses on the digital economy. His research is mainly on China's political economy. Learn more about your ad choices. Visit megaphone.fm/adchoices

Disruption resulting from the proliferation of AI is coming. The authors of the bestselling Prediction Machines describe what you can do to prepare. Banking and finance, pharmaceuticals, automotive, medical technology, retail. Artificial intelligence (AI) has made its way into many industries around the world. But the truth is, it has just begun its odyssey toward cheaper, better, and faster predictions to drive strategic business decisions--powering and accelerating business. When prediction is taken to the max, industries transform. The disruption that comes with such transformation is yet to be felt--but it is coming. How do businesses prepare?  In Prediction Machines, eminent economists Ajay Agrawal, Joshua Gans, and Avi Goldfarb explained the simple yet game-changing economics of AI. Now, in Power and Prediction: The Disruptive Economics of Artificial Intelligence (HBR Press, 2022), they go further to reveal AI as a prediction technology directly impacting decision-making and to teach businesses how to identify disruptive opportunities and threats resulting from AI. Their exhaustive study of new developments in artificial intelligence and the past history of how technologies have disrupted industries highlights the striking phase we are now in: after witnessing the power of this new technology and before its widespread adoption--what they call "the Between Times." While there continue to be important opportunities for businesses, there are also threats of disruption. As prediction machines improve, old ways of doing things will be upended. Also, the process by which AI filters into the many systems involved in application is very uneven. That process will have winners and losers. How can businesses leverage, or protect, their positions? Filled with illuminating insights, rich examples, and practical advice, Power and Prediction is the must-read guide for any business leader or policy maker on how to make the coming AI disruptions work for you rather than against you. Interviewee Avi Goldfarb is the Rotman Chair in Artificial Intelligence and Healthcare and a professor of marketing at the Rotman School of Management, University of Toronto. Avi is also Chief Data Scientist at the Creative Destruction Lab and the CDL Rapid Screening Consortium, a faculty affiliate at the Vector Institute and the Schwartz-Reisman Institute for Technology and Society, and a Research Associate at the National Bureau of Economic Research. Avi's research focuses on the opportunities and challenges of the digital economy. He has published academic articles in marketing, statistics, law, management, medicine, political science, refugee studies, physics, computing, and economics. Avi is a former Senior Editor at Marketing Science. His work on online advertising won the INFORMS Society of Marketing Science Long Term Impact Award. He testified before the U.S. Senate Judiciary Committee on competition and privacy in digital advertising. His work has been referenced in White House reports, European Commission documents, the New York Times, the Economist, and elsewhere. Peter Lorentzen is economics professor at the University of San Francisco. He heads USF's Applied Economics Master's program, which focuses on the digital economy. His research is mainly on China's political economy. Learn more about your ad choices. Visit megaphone.fm/adchoices

Disruption resulting from the proliferation of AI is coming. The authors of the bestselling Prediction Machines describe what you can do to prepare. Banking and finance, pharmaceuticals, automotive, medical technology, retail. Artificial intelligence (AI) has made its way into many industries around the world. But the truth is, it has just begun its odyssey toward cheaper, better, and faster predictions to drive strategic business decisions--powering and accelerating business. When prediction is taken to the max, industries transform. The disruption that comes with such transformation is yet to be felt--but it is coming. How do businesses prepare?  In Prediction Machines, eminent economists Ajay Agrawal, Joshua Gans, and Avi Goldfarb explained the simple yet game-changing economics of AI. Now, in Power and Prediction: The Disruptive Economics of Artificial Intelligence (HBR Press, 2022), they go further to reveal AI as a prediction technology directly impacting decision-making and to teach businesses how to identify disruptive opportunities and threats resulting from AI. Their exhaustive study of new developments in artificial intelligence and the past history of how technologies have disrupted industries highlights the striking phase we are now in: after witnessing the power of this new technology and before its widespread adoption--what they call "the Between Times." While there continue to be important opportunities for businesses, there are also threats of disruption. As prediction machines improve, old ways of doing things will be upended. Also, the process by which AI filters into the many systems involved in application is very uneven. That process will have winners and losers. How can businesses leverage, or protect, their positions? Filled with illuminating insights, rich examples, and practical advice, Power and Prediction is the must-read guide for any business leader or policy maker on how to make the coming AI disruptions work for you rather than against you. Interviewee Avi Goldfarb is the Rotman Chair in Artificial Intelligence and Healthcare and a professor of marketing at the Rotman School of Management, University of Toronto. Avi is also Chief Data Scientist at the Creative Destruction Lab and the CDL Rapid Screening Consortium, a faculty affiliate at the Vector Institute and the Schwartz-Reisman Institute for Technology and Society, and a Research Associate at the National Bureau of Economic Research. Avi's research focuses on the opportunities and challenges of the digital economy. He has published academic articles in marketing, statistics, law, management, medicine, political science, refugee studies, physics, computing, and economics. Avi is a former Senior Editor at Marketing Science. His work on online advertising won the INFORMS Society of Marketing Science Long Term Impact Award. He testified before the U.S. Senate Judiciary Committee on competition and privacy in digital advertising. His work has been referenced in White House reports, European Commission documents, the New York Times, the Economist, and elsewhere. Peter Lorentzen is economics professor at the University of San Francisco. He heads USF's Applied Economics Master's program, which focuses on the digital economy. His research is mainly on China's political economy. Learn more about your ad choices. Visit megaphone.fm/adchoices

Disruption resulting from the proliferation of AI is coming. The authors of the bestselling Prediction Machines describe what you can do to prepare. Banking and finance, pharmaceuticals, automotive, medical technology, retail. Artificial intelligence (AI) has made its way into many industries around the world. But the truth is, it has just begun its odyssey toward cheaper, better, and faster predictions to drive strategic business decisions--powering and accelerating business. When prediction is taken to the max, industries transform. The disruption that comes with such transformation is yet to be felt--but it is coming. How do businesses prepare?  In Prediction Machines, eminent economists Ajay Agrawal, Joshua Gans, and Avi Goldfarb explained the simple yet game-changing economics of AI. Now, in Power and Prediction: The Disruptive Economics of Artificial Intelligence (HBR Press, 2022), they go further to reveal AI as a prediction technology directly impacting decision-making and to teach businesses how to identify disruptive opportunities and threats resulting from AI. Their exhaustive study of new developments in artificial intelligence and the past history of how technologies have disrupted industries highlights the striking phase we are now in: after witnessing the power of this new technology and before its widespread adoption--what they call "the Between Times." While there continue to be important opportunities for businesses, there are also threats of disruption. As prediction machines improve, old ways of doing things will be upended. Also, the process by which AI filters into the many systems involved in application is very uneven. That process will have winners and losers. How can businesses leverage, or protect, their positions? Filled with illuminating insights, rich examples, and practical advice, Power and Prediction is the must-read guide for any business leader or policy maker on how to make the coming AI disruptions work for you rather than against you. Interviewee Avi Goldfarb is the Rotman Chair in Artificial Intelligence and Healthcare and a professor of marketing at the Rotman School of Management, University of Toronto. Avi is also Chief Data Scientist at the Creative Destruction Lab and the CDL Rapid Screening Consortium, a faculty affiliate at the Vector Institute and the Schwartz-Reisman Institute for Technology and Society, and a Research Associate at the National Bureau of Economic Research. Avi's research focuses on the opportunities and challenges of the digital economy. He has published academic articles in marketing, statistics, law, management, medicine, political science, refugee studies, physics, computing, and economics. Avi is a former Senior Editor at Marketing Science. His work on online advertising won the INFORMS Society of Marketing Science Long Term Impact Award. He testified before the U.S. Senate Judiciary Committee on competition and privacy in digital advertising. His work has been referenced in White House reports, European Commission documents, the New York Times, the Economist, and elsewhere. Peter Lorentzen is economics professor at the University of San Francisco. He heads USF's Applied Economics Master's program, which focuses on the digital economy. His research is mainly on China's political economy. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/finance

Disruption resulting from the proliferation of AI is coming. The authors of the bestselling Prediction Machines describe what you can do to prepare. Banking and finance, pharmaceuticals, automotive, medical technology, retail. Artificial intelligence (AI) has made its way into many industries around the world. But the truth is, it has just begun its odyssey toward cheaper, better, and faster predictions to drive strategic business decisions--powering and accelerating business. When prediction is taken to the max, industries transform. The disruption that comes with such transformation is yet to be felt--but it is coming. How do businesses prepare?  In Prediction Machines, eminent economists Ajay Agrawal, Joshua Gans, and Avi Goldfarb explained the simple yet game-changing economics of AI. Now, in Power and Prediction: The Disruptive Economics of Artificial Intelligence (HBR Press, 2022), they go further to reveal AI as a prediction technology directly impacting decision-making and to teach businesses how to identify disruptive opportunities and threats resulting from AI. Their exhaustive study of new developments in artificial intelligence and the past history of how technologies have disrupted industries highlights the striking phase we are now in: after witnessing the power of this new technology and before its widespread adoption--what they call "the Between Times." While there continue to be important opportunities for businesses, there are also threats of disruption. As prediction machines improve, old ways of doing things will be upended. Also, the process by which AI filters into the many systems involved in application is very uneven. That process will have winners and losers. How can businesses leverage, or protect, their positions? Filled with illuminating insights, rich examples, and practical advice, Power and Prediction is the must-read guide for any business leader or policy maker on how to make the coming AI disruptions work for you rather than against you. Interviewee Avi Goldfarb is the Rotman Chair in Artificial Intelligence and Healthcare and a professor of marketing at the Rotman School of Management, University of Toronto. Avi is also Chief Data Scientist at the Creative Destruction Lab and the CDL Rapid Screening Consortium, a faculty affiliate at the Vector Institute and the Schwartz-Reisman Institute for Technology and Society, and a Research Associate at the National Bureau of Economic Research. Avi's research focuses on the opportunities and challenges of the digital economy. He has published academic articles in marketing, statistics, law, management, medicine, political science, refugee studies, physics, computing, and economics. Avi is a former Senior Editor at Marketing Science. His work on online advertising won the INFORMS Society of Marketing Science Long Term Impact Award. He testified before the U.S. Senate Judiciary Committee on competition and privacy in digital advertising. His work has been referenced in White House reports, European Commission documents, the New York Times, the Economist, and elsewhere. Peter Lorentzen is economics professor at the University of San Francisco. He heads USF's Applied Economics Master's program, which focuses on the digital economy. His research is mainly on China's political economy. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/science-technology-and-society

Disruption resulting from the proliferation of AI is coming. The authors of the bestselling Prediction Machines describe what you can do to prepare. Banking and finance, pharmaceuticals, automotive, medical technology, retail. Artificial intelligence (AI) has made its way into many industries around the world. But the truth is, it has just begun its odyssey toward cheaper, better, and faster predictions to drive strategic business decisions--powering and accelerating business. When prediction is taken to the max, industries transform. The disruption that comes with such transformation is yet to be felt--but it is coming. How do businesses prepare?  In Prediction Machines, eminent economists Ajay Agrawal, Joshua Gans, and Avi Goldfarb explained the simple yet game-changing economics of AI. Now, in Power and Prediction: The Disruptive Economics of Artificial Intelligence (HBR Press, 2022), they go further to reveal AI as a prediction technology directly impacting decision-making and to teach businesses how to identify disruptive opportunities and threats resulting from AI. Their exhaustive study of new developments in artificial intelligence and the past history of how technologies have disrupted industries highlights the striking phase we are now in: after witnessing the power of this new technology and before its widespread adoption--what they call "the Between Times." While there continue to be important opportunities for businesses, there are also threats of disruption. As prediction machines improve, old ways of doing things will be upended. Also, the process by which AI filters into the many systems involved in application is very uneven. That process will have winners and losers. How can businesses leverage, or protect, their positions? Filled with illuminating insights, rich examples, and practical advice, Power and Prediction is the must-read guide for any business leader or policy maker on how to make the coming AI disruptions work for you rather than against you. Interviewee Avi Goldfarb is the Rotman Chair in Artificial Intelligence and Healthcare and a professor of marketing at the Rotman School of Management, University of Toronto. Avi is also Chief Data Scientist at the Creative Destruction Lab and the CDL Rapid Screening Consortium, a faculty affiliate at the Vector Institute and the Schwartz-Reisman Institute for Technology and Society, and a Research Associate at the National Bureau of Economic Research. Avi's research focuses on the opportunities and challenges of the digital economy. He has published academic articles in marketing, statistics, law, management, medicine, political science, refugee studies, physics, computing, and economics. Avi is a former Senior Editor at Marketing Science. His work on online advertising won the INFORMS Society of Marketing Science Long Term Impact Award. He testified before the U.S. Senate Judiciary Committee on competition and privacy in digital advertising. His work has been referenced in White House reports, European Commission documents, the New York Times, the Economist, and elsewhere. Peter Lorentzen is economics professor at the University of San Francisco. He heads USF's Applied Economics Master's program, which focuses on the digital economy. His research is mainly on China's political economy. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/economics

Disruption resulting from the proliferation of AI is coming. The authors of the bestselling Prediction Machines describe what you can do to prepare. Banking and finance, pharmaceuticals, automotive, medical technology, retail. Artificial intelligence (AI) has made its way into many industries around the world. But the truth is, it has just begun its odyssey toward cheaper, better, and faster predictions to drive strategic business decisions--powering and accelerating business. When prediction is taken to the max, industries transform. The disruption that comes with such transformation is yet to be felt--but it is coming. How do businesses prepare?  In Prediction Machines, eminent economists Ajay Agrawal, Joshua Gans, and Avi Goldfarb explained the simple yet game-changing economics of AI. Now, in Power and Prediction: The Disruptive Economics of Artificial Intelligence (HBR Press, 2022), they go further to reveal AI as a prediction technology directly impacting decision-making and to teach businesses how to identify disruptive opportunities and threats resulting from AI. Their exhaustive study of new developments in artificial intelligence and the past history of how technologies have disrupted industries highlights the striking phase we are now in: after witnessing the power of this new technology and before its widespread adoption--what they call "the Between Times." While there continue to be important opportunities for businesses, there are also threats of disruption. As prediction machines improve, old ways of doing things will be upended. Also, the process by which AI filters into the many systems involved in application is very uneven. That process will have winners and losers. How can businesses leverage, or protect, their positions? Filled with illuminating insights, rich examples, and practical advice, Power and Prediction is the must-read guide for any business leader or policy maker on how to make the coming AI disruptions work for you rather than against you. Interviewee Avi Goldfarb is the Rotman Chair in Artificial Intelligence and Healthcare and a professor of marketing at the Rotman School of Management, University of Toronto. Avi is also Chief Data Scientist at the Creative Destruction Lab and the CDL Rapid Screening Consortium, a faculty affiliate at the Vector Institute and the Schwartz-Reisman Institute for Technology and Society, and a Research Associate at the National Bureau of Economic Research. Avi's research focuses on the opportunities and challenges of the digital economy. He has published academic articles in marketing, statistics, law, management, medicine, political science, refugee studies, physics, computing, and economics. Avi is a former Senior Editor at Marketing Science. His work on online advertising won the INFORMS Society of Marketing Science Long Term Impact Award. He testified before the U.S. Senate Judiciary Committee on competition and privacy in digital advertising. His work has been referenced in White House reports, European Commission documents, the New York Times, the Economist, and elsewhere. Peter Lorentzen is economics professor at the University of San Francisco. He heads USF's Applied Economics Master's program, which focuses on the digital economy. His research is mainly on China's political economy. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/technology

Disruption resulting from the proliferation of AI is coming. The authors of the bestselling Prediction Machines describe what you can do to prepare. Banking and finance, pharmaceuticals, automotive, medical technology, retail. Artificial intelligence (AI) has made its way into many industries around the world. But the truth is, it has just begun its odyssey toward cheaper, better, and faster predictions to drive strategic business decisions--powering and accelerating business. When prediction is taken to the max, industries transform. The disruption that comes with such transformation is yet to be felt--but it is coming. How do businesses prepare?  In Prediction Machines, eminent economists Ajay Agrawal, Joshua Gans, and Avi Goldfarb explained the simple yet game-changing economics of AI. Now, in Power and Prediction: The Disruptive Economics of Artificial Intelligence (HBR Press, 2022), they go further to reveal AI as a prediction technology directly impacting decision-making and to teach businesses how to identify disruptive opportunities and threats resulting from AI. Their exhaustive study of new developments in artificial intelligence and the past history of how technologies have disrupted industries highlights the striking phase we are now in: after witnessing the power of this new technology and before its widespread adoption--what they call "the Between Times." While there continue to be important opportunities for businesses, there are also threats of disruption. As prediction machines improve, old ways of doing things will be upended. Also, the process by which AI filters into the many systems involved in application is very uneven. That process will have winners and losers. How can businesses leverage, or protect, their positions? Filled with illuminating insights, rich examples, and practical advice, Power and Prediction is the must-read guide for any business leader or policy maker on how to make the coming AI disruptions work for you rather than against you. Interviewee Avi Goldfarb is the Rotman Chair in Artificial Intelligence and Healthcare and a professor of marketing at the Rotman School of Management, University of Toronto. Avi is also Chief Data Scientist at the Creative Destruction Lab and the CDL Rapid Screening Consortium, a faculty affiliate at the Vector Institute and the Schwartz-Reisman Institute for Technology and Society, and a Research Associate at the National Bureau of Economic Research. Avi's research focuses on the opportunities and challenges of the digital economy. He has published academic articles in marketing, statistics, law, management, medicine, political science, refugee studies, physics, computing, and economics. Avi is a former Senior Editor at Marketing Science. His work on online advertising won the INFORMS Society of Marketing Science Long Term Impact Award. He testified before the U.S. Senate Judiciary Committee on competition and privacy in digital advertising. His work has been referenced in White House reports, European Commission documents, the New York Times, the Economist, and elsewhere. Peter Lorentzen is economics professor at the University of San Francisco. He heads USF's Applied Economics Master's program, which focuses on the digital economy. His research is mainly on China's political economy. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/public-policy

Jeffrey Carpenter and Andrea Robbett's book Game Theory and Behavior (MIT Press, 2022) is an introduction to game theory that offers not only theoretical tools but also the intuition and behavioral insights to apply these tools to real-world situations. This introductory text on game theory provides students with both the theoretical tools to analyze situations through the logic of game theory and the intuition and behavioral insights to apply these tools to real-world situations. It is unique among game theory texts in offering a clear, formal introduction to standard game theory while incorporating evidence from experimental data and introducing recent behavioral models. Students will not only learn about incentives, how to represent situations as games, and what agents “should” do in these situations, but they will also be presented with evidence that either confirms the theoretical assumptions or suggests a way in which the theory might be updated. Jeffrey Carpenter is the James Jermain Professor of Political Economy at Middlebury College. His research interests include Experimental and Behavioral Economics with applications to Labor, Public and Development Economics. While pursuing these interests he has conducted lab and field experiments in North America, South America, Europe and Asia. Andrea Robbett is an Associate Professor of Economics at Middlebury College. Her research uses laboratory experiments to test canonical theoretical models, new ideas, and conventional wisdom. Her work has addressed topics in public economics, labor, voting, information avoidance, financial decision-making and "attribute overload," trust and cooperation, and auctions. Peter Lorentzen is economics professor at the University of San Francisco. He heads USF's Applied Economics Master's program, which focuses on the digital economy. His research is mainly on China's political economy. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/psychology

Jeffrey Carpenter and Andrea Robbett's book Game Theory and Behavior (MIT Press, 2022) is an introduction to game theory that offers not only theoretical tools but also the intuition and behavioral insights to apply these tools to real-world situations. This introductory text on game theory provides students with both the theoretical tools to analyze situations through the logic of game theory and the intuition and behavioral insights to apply these tools to real-world situations. It is unique among game theory texts in offering a clear, formal introduction to standard game theory while incorporating evidence from experimental data and introducing recent behavioral models. Students will not only learn about incentives, how to represent situations as games, and what agents “should” do in these situations, but they will also be presented with evidence that either confirms the theoretical assumptions or suggests a way in which the theory might be updated. Jeffrey Carpenter is the James Jermain Professor of Political Economy at Middlebury College. His research interests include Experimental and Behavioral Economics with applications to Labor, Public and Development Economics. While pursuing these interests he has conducted lab and field experiments in North America, South America, Europe and Asia. Andrea Robbett is an Associate Professor of Economics at Middlebury College. Her research uses laboratory experiments to test canonical theoretical models, new ideas, and conventional wisdom. Her work has addressed topics in public economics, labor, voting, information avoidance, financial decision-making and "attribute overload," trust and cooperation, and auctions. Peter Lorentzen is economics professor at the University of San Francisco. He heads USF's Applied Economics Master's program, which focuses on the digital economy. His research is mainly on China's political economy. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/economics

Jeffrey Carpenter and Andrea Robbett's book Game Theory and Behavior (MIT Press, 2022) is an introduction to game theory that offers not only theoretical tools but also the intuition and behavioral insights to apply these tools to real-world situations. This introductory text on game theory provides students with both the theoretical tools to analyze situations through the logic of game theory and the intuition and behavioral insights to apply these tools to real-world situations. It is unique among game theory texts in offering a clear, formal introduction to standard game theory while incorporating evidence from experimental data and introducing recent behavioral models. Students will not only learn about incentives, how to represent situations as games, and what agents “should” do in these situations, but they will also be presented with evidence that either confirms the theoretical assumptions or suggests a way in which the theory might be updated. Jeffrey Carpenter is the James Jermain Professor of Political Economy at Middlebury College. His research interests include Experimental and Behavioral Economics with applications to Labor, Public and Development Economics. While pursuing these interests he has conducted lab and field experiments in North America, South America, Europe and Asia. Andrea Robbett is an Associate Professor of Economics at Middlebury College. Her research uses laboratory experiments to test canonical theoretical models, new ideas, and conventional wisdom. Her work has addressed topics in public economics, labor, voting, information avoidance, financial decision-making and "attribute overload," trust and cooperation, and auctions. Peter Lorentzen is economics professor at the University of San Francisco. He heads USF's Applied Economics Master's program, which focuses on the digital economy. His research is mainly on China's political economy. Learn more about your ad choices. Visit megaphone.fm/adchoices